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Plus ça change - evolutionary sequence divergence predicts protein subcellular localization signals.

Yoshinori Fukasawa, Ross K K Leung, Stephen K W Tsui

  • 1Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Japan. horton-p@aist.go.jp.

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Summary
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Sequence divergence, a measure of evolutionary change, can predict protein subcellular localization. This novel feature shows promise, especially for identifying proteins with unusual targeting signals, even when combined with traditional methods.

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Area of Science:

  • Cell Biology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Protein subcellular localization is crucial for cell function and a major research area.
  • Previous prediction methods used features like amino acid composition and sequence similarity.
  • Sequence conservation of sorting motifs, despite its success elsewhere, was underutilized for localization prediction.

Purpose of the Study:

  • To explore the potential of sequence divergence as a novel feature for protein localization prediction.
  • To assess the effectiveness of evolutionary sequence divergence in identifying N-terminal sorting sequences across different species.

Main Methods:

  • Utilized yeast, mammal, and plant datasets to analyze protein sequences.
  • Computed evolutionary sequence divergence on both automatically defined and hand-curated ortholog sets.
  • Evaluated the predictive power of sequence divergence alone and in combination with traditional features like amino acid composition.

Main Results:

  • Evolutionary sequence divergence alone demonstrated significant power in predicting N-terminal sorting sequences.
  • Sequence divergence performance was comparable across automatically defined and curated ortholog sets.
  • Combining sequence divergence with traditional features did not consistently improve classification performance, but identified atypical targeting signals and potential misannotations.

Conclusions:

  • This study provides the first quantitative assessment of evolutionary sequence divergence for protein localization prediction.
  • While not a simple addition to improve overall accuracy, sequence divergence is a valuable feature.
  • Sequence divergence shows potential as a complementary tool, succeeding in cases where other methods fail.